FIELD OF THE INVENTION The present invention generally relates to the field of extended reality education. In particular, the present invention is directed to an apparatus and method for an education platform and ecosystem using extended reality.
BACKGROUND Currently there are over 17 million online education learners in the United States, however, online education systems lack personalization and interaction with learners often finding it difficult to focus. An effective online education system remains an elusive goal.
SUMMARY OF THE DISCLOSURE In an aspect, an apparatus for an education platform and ecosystem using extended reality the apparatus comprising at least a processor and a memory communicatively connected to the processor, the memory containing instructions configuring the at least a processor to receive entity data associated with an entity, generate an educational model, wherein the educational is configured to receive at least an input from a user and generate at least an educational response as a function of the at least an input, instantiate, in a user interface, a virtual avatar, wherein the virtual avatar comprises at least a base image and a plurality of animations of base image, wherein the virtual avatar is configured to receive the at least an educational response and display an animation of the plurality of animations as a function of the at least an educational response and generate a non-fungible token linked to the entity data and the virtual avatar.
In another aspect, a method for an education platform and ecosystem using extended reality, the method comprising receiving, by at least a processor, entity data associated with an entity, generating, by the at least a processor, an educational model, wherein the educational model is configured to receive at least an input from a user and generate at least an educational response as a function of the at least an input; and instantiating, by the at least a processor, in a user interface, a virtual avatar, wherein the virtual avatar comprises at least a base image and a plurality of animations of base image, wherein the virtual avatar is configured to receive the at least an educational response and display an animation of the plurality of animations as a function of the at least an educational response and generating, by the at least a processor, a non-fungible token linked to the entity data and the virtual avatar.
These and other aspects and features of non-limiting embodiments of the present invention will become apparent to those skilled in the art upon review of the following description of specific non-limiting embodiments of the invention in conjunction with the accompanying drawings.
BRIEF DESCRIPTION OF THE DRAWINGS For the purpose of illustrating the invention, the drawings show aspects of one or more embodiments of the invention. However, it should be understood that the present invention is not limited to the precise arrangements and instrumentalities shown in the drawings, wherein:
FIG. 1 is a block diagram illustrating an exemplary embodiment of an apparatus for an education platform and ecosystem using extended reality;
FIG. 2 is a block diagram illustrating an exemplary embodiment of an education data database;
FIG. 3 is a block diagram illustrating an exemplary embodiment of an immutable sequential listing;
FIG. 4 is a block diagram illustrating an exemplary embodiment of a machine learning model;
FIG. 5 is a flow diagram illustrating an exemplary embodiment of a method for an education platform and ecosystem using extended reality and
FIG. 6 is a block diagram of a computing system that can be used to implement any one or more of the methodologies disclosed herein and any one or more portions thereof.
The drawings are not necessarily to scale and may be illustrated by phantom lines, diagrammatic representations, and fragmentary views. In certain instances, details that are not necessary for an understanding of the embodiments or that render other details difficult to perceive may have been omitted.
DETAILED DESCRIPTION At a high level, embodiments disclosed herein exemplify an apparatus for an education platform and ecosystem using extended reality. In an embodiment, apparatus is configured to receive entity data pertaining to an entity and generate an educational model and virtual avatar. A non-fungible token may be generated based on the entity data and the virtual avatar.
Referring now to FIG. 1, an exemplary embodiment of an apparatus 100 for an education platform using extended reality is illustrated. Apparatus 100 includes a computing device 104. Computing device 104 may include any computing device 104 as described in this disclosure, including without limitation a microcontroller, microprocessor, digital signal processor (DSP) and/or system on a chip (SoC) as described in this disclosure. Computing device 104 may include, be included in, and/or communicate with a mobile device such as a mobile telephone or smartphone. Computing device 104 may include a single computing device 104 operating independently or May include two or more computing device 104 operating in concert, in parallel, sequentially or the like; two or more computing devices 104 may be included together in a single computing device 104 or in two or more computing devices 104. Computing device 104 may interface or communicate with one or more additional devices as described below in further detail via a network interface device. Network interface device may be utilized for connecting computing device 104 to one or more of a variety of networks, and one or more devices. Examples of a network interface device include, but are not limited to, a network interface card (e.g., a mobile network interface card, a LAN card), a modem, and any combination thereof. Examples of a network include, but are not limited to, a wide area network (e.g., the Internet, an enterprise network), a local area network (e.g., a network associated with an office, a building, a campus or other relatively small geographic space), a telephone network, a data network associated with a telephone/voice provider (e.g., a mobile communications provider data and/or voice network), a direct connection between two computing devices 104, and any combinations thereof. A network may employ a wired and/or a wireless mode of communication. In general, any network topology may be used. Information (e.g., data, software etc.) may be communicated to and/or from a computer and/or a computing device 104. Computing device 104 may include but is not limited to, for example, a computing device 104 or cluster of computing devices 104 in a first location and a second computing device 104 or cluster of computing devices 104 in a second location. Computing device 104 may include one or more computing devices 104 dedicated to data storage, security, distribution of traffic for load balancing, and the like. Computing device 104 may distribute one or more computing tasks as described below across a plurality of computing devices 104 of computing device 104, which may operate in parallel, in series, redundantly, or in any other manner used for distribution of tasks or memory between computing devices 104. Computing device 104 may be implemented using a “shared nothing” architecture in which data is cached at the worker; in an embodiment, this may enable scalability of apparatus 100 and/or computing device 104.
Still referring to FIG. 1, computing device 104 may be designed and/or configured to perform any method, method step, or sequence of method steps in any embodiment described in this disclosure, in any order and with any degree of repetition. For instance, computing device 104 may be configured to perform a single step or sequence repeatedly until a desired or commanded outcome is achieved; repetition of a step or a sequence of steps may be performed iteratively and/or recursively using outputs of previous repetitions as inputs to subsequent repetitions, aggregating inputs and/or outputs of repetitions to produce an aggregate result, reduction or decrement of one or more variables such as global variables, and/or division of a larger processing task into a set of iteratively addressed smaller processing tasks. Computing device 104 may perform any step or sequence of steps as described in this disclosure in parallel, such as simultaneously and/or substantially simultaneously performing a step two or more times using two or more parallel threads, processor cores, or the like; division of tasks between parallel threads and/or processes may be performed according to any protocol suitable for division of tasks between iterations. Persons skilled in the art, upon reviewing the entirety of this disclosure, will be aware of various ways in which steps, sequences of steps, processing tasks, and/or data may be subdivided, shared, or otherwise dealt with using iteration, recursion, and/or parallel processing.
Still in reference to FIG. 1, apparatus 100 may be configured to receive entity data 120 associated with an entity. “Entity data” as used in this disclosure is defined as data relating to a user/entity who will be associated with the virtual avatar, for example, a student or teacher. Entity data may include a plurality of entity data items. Entity data items may be, without limitation, the user's name, gender, height, weight, location, eye color, hair color, educational history, and the like. “Educational history” as used in this disclosure is defined as any formal or information education a user has received throughout their lifetime. For example, educational history may include the most recent grade level a user has completed in a formal schooling setting, a high school degree, an undergraduate degree, a graduate degree, and the like. Entity data 120 may be received from user input device 112. “User input device” may include without limitation, a display in communication with computing device 104, where a display may include any display as described herein. User input device 112 may include an additional computing device, such as a mobile device, laptop, desktop, computer, and the like. In an embodiment, user input device 112 may have a touch screen to interact with the user. User may input user data through user input device 112.
Still in reference to FIG. 1, apparatus 100 may be configured to generate an educational model 124, wherein the educational model 124 is configured to receive at least an input from a user and generate at least an educational response as a function of the at least an input. In an embodiment, inputs from the user may include, without limitation, textual input, audio input, video input. “Educational model” as used in this disclosure is defined as a component in communication with at least one virtual avatar model and configured to communicate the educational response to at least one virtual avatar. In an embodiment, educational model 124 may be hosted in a decentralized platform (i.e., web 3.0). “Educational response” as used in this disclosure is defined as educational information that teaches entities on one or more subjects. For example, educational response May include information relating to mathematics which may include information such as basic arithmetic operations or calculations such as addition, subtraction, multiplication and division, which are taught to us in primary classes. By way of another example, the education response may also include more advanced information such as geometry, factors, ratios, and the like. Educational response may be stored in a data store such as a database, knowledge base, and the like. In an embodiment, the communication of educational response may include utilizing blockchain technology. Blockchain technology is further discussed in FIG. 3 below. In another embodiment, communication between the educational model 124 and the at least one virtual avatar 128 and/or virtual avatar model may be in real time. For example, the communications above may be in high time resolution (i.e., low latency). “High time resolution” as used in this disclosure is defined as a computer network that is optimized to process a very high volume of data messages with minimal delay (latency). In an embodiment, the entity data 120 and a machine-learning module may be utilized to generate a user education report. The user education report may be used to access a starting point for courses for a user, education level, and the like. In an embodiment, the user education report may include assessments related to reading, science, math and the like.
Still referring to FIG. 1, educational model 124 may include a chatbot which may produce responses. Educational model 124 may receive input from a user/student by the use of a chatbot. Educational model 124 may also receive input with a lookup table and/or database. A “chatbot” as used in this disclosure is defined as a software application used to conduct an on-line chat conversation via text, text-to-speech, and/or speech-to-text, in lieu of providing direct contact with a live human agent. For example, communication with the student/user may be handled by the chatbot. The chatbot may use a decision tree, data base, or other data structure to respond to the users entry into the chatbot as a function of a chatbot input. As used in the current disclosure, “chatbot input” is any response that a user inputs into a chatbot as a response to a prompt or question. A “decision tree,” as used in this disclosure, is a data structure that represents and combines one or more determinations or other computations based on and/or concerning data provided thereto, as well as earlier such determinations or calculations, as nodes of a tree data structure where inputs of some nodes are connected to outputs of others. In an embodiment, a classifier, look up table, database may be utilized to make a decision at any or all nodes of the decision tree. In an embodiment, a decision tree may be trained by training the nodes with classifiers and/or classifier instances. For example, if a classifier output is Y the decision may be to go to node A1 and if a classifier output is X the decision may be to go to node A2. In an embodiment, apparatus may be configured to extract answer datum from a student/user using a chatbot. A chatbot may accomplish this by presenting the student/user with questions. Answer datum may be generated as a function of the student/user response. In the embodiments, a chatbot is designed to convincingly simulate the way a human would behave/respond as a conversational partner. A classifier machine learning model may be configured to generate chatbot responses as a function of answer datum as an input and output additional questions. In an embodiment, a chatbot may be configured to ask questions from the questionnaire to a student/user and generate a response as a function of the answer datum and output additional questions. Additionally, a chatbot may be configured to respond to the student/user based on the student/user's responses. In other embodiments, a chatbot may prompt the user to respond to the questions in text or a video format. For example, a chatbot may verbally ask the student questions prompting a student to submit a video response. A chatbot then may convert the student's verbal responses to text. In an embodiment, the chatbot classifier machine learning model may be supervised and trained with training data. Training data may include a database of answer data elements and question data elements. The training data may correlate the answer data as a function of the student/user response (e.g., response data). In another embodiment, the chatbot classifier may be retrained utilizing the user input. The user input may include obtaining the user input, turning the user input into training data and then using the updated training data to retrain the chatbot classifier. Chatbot classifier may be any classifier trained by an algorithm, as described throughout this disclosure.
With continued reference to FIG. 1, the chatbot may classify students using chatbot input. A computing device 104 may be configured to analyze a student's input into the chatbot. The chatbot may be able to analyze a student's text entries based on pre-determined set of factors, key words, composition, and clarity of student responses. In embodiments, a computing device 104 may search for a synonyms or other equivalent words to the keywords and correlate those responses to the keywords. Additionally, students may be classified according to the chatbot input. Student classification may occur by identifying keywords within a student's response and/or by employing a machine learning model in the form of a chatbot input classifier. A keyword as it relates to a student may be any word that identifies a student's skill, ability, and/or aptitude to fulfill the education requirement. A chatbot input classifier may be trained using past chatbot inputs, job answer datum, and/or entity datum.
With continued reference to FIG. 1, chatbot may output the next question based upon the chatbot input. Students may be presented with various types of questions as a function of classification using a chatbot input. Each of the student's responses will be input into the chatbot input classifier or analysis tools, creating a feedback loop. In embodiments, the questions may dig deeper into a student's credentials or educational history. The chatbot may continue to produce questions about any given fact about the student until a terminal response is received or all of the questions run out. A student response may also trigger the chatbot to inquire about a different topic form the student. A terminal response is a response that will end the interview a prompt the student to close and or exit the chat bot. In embodiments, the chatbot generated questions may also be predetermined. This new process may occur by using a decision tree or other data structures.
With continuing reference to FIG. 1, computing device 104 may be configured to the respond to a chatbot input using a decision tree A “decision tree,” as used in this disclosure, is a data structure that represents and combines one or more determinations or other computations based on and/or concerning data provided thereto, as well as earlier such determinations or calculations, as nodes of a tree data structure where inputs of some nodes are connected to outputs of others. Decision tree may have at least a root node, or node that receives data input to the decision tree, corresponding to at least a student input into a chatbot. Decision tree has at least a terminal node, which may alternatively or additionally be referred to herein as a “leaf node,” corresponding to at least an exit indication; in other words, decision and/or determinations produced by decision tree may be output at the at least a terminal node. Decision tree may include one or more internal nodes, defined as nodes connecting outputs of root nodes to inputs of terminal nodes. Computing device may generate two or more decision trees, which may overlap; for instance, a root node of one tree may connect to and/or receive output from one or more terminal nodes of another tree, intermediate nodes of one tree may be shared with another tree, or the like.
Still referring to FIG. 1, computing device 104 may build decision tree by following relational identification; for example, relational indication may specify that a first rule module receives an input from at least a second rule module and generates an output to at least a third rule module, and so forth, which may indicate to computing device and in which such rule modules will be placed in decision tree. Building decision tree may include recursively performing mapping of execution results output by one tree and/or subtree to root nodes of another tree and/or subtree, for instance by using such execution results as execution parameters of a subtree. In this manner, computing device 104 may generate connections and/or combinations of one or more trees to one another to define overlaps and/or combinations into larger trees and/or combinations thereof. Such connections and/or combinations may be displayed by visual interface to user, for instance in first view, to enable viewing, editing, selection, and/or deletion by user; connections and/or combinations generated thereby may be highlighted, for instance using a different color, a label, and/or other form of emphasis aiding in identification by a user. In some embodiments, subtrees, previously constructed trees, and/or entire data structures may be represented and/or converted to rule modules, with graphical models representing them, and which may then be used in further iterations or steps of generation of decision tree and/or data structure. Alternatively or additionally subtrees, previously constructed trees, and/or entire data structures may be converted to APIs to interface with further iterations or steps of methods as described in this disclosure. As a further example, such subtrees, previously constructed trees, and/or entire data structures may become remote resources to which further iterations or steps of data structures and/or decision trees may transmit data and from which further iterations or steps of generation of data structure receive data, for instance as part of a decision in a given decision tree node.
Continuing to refer to FIG. 1, decision tree may incorporate one or more manually entered or otherwise provided decision criteria. Decision tree may incorporate one or more decision criteria using an application programmer interface (API). Decision tree may establish a link to a remote decision module, device, system, or the like. Decision tree may perform one or more database lookups and/or look-up table lookups. Decision tree may include at least a decision calculation module, which may be imported via an API, by incorporation of a program module in source code, executable, or other form, and/or linked to a given node by establishing a communication interface with one or more exterior processes, programs, systems, remote devices, or the like; for instance, where a user operating system has a previously existent calculation and/or decision engine configured to make a decision corresponding to a given node, for instance and without limitation using one or more elements of domain knowledge, by receiving an input and producing an output representing a decision, a node may be configured to provide data to the input and receive the output representing the decision, based upon which the node may perform its decision.
Still referring to FIG. 1, apparatus 100 may be configured to instantiate, in a user interface, a virtual avatar 128. In an embodiment, the virtual avatar may include a base image and a plurality of animations of the base image, wherein the virtual avatar may be configured to receive an educational response and display an animation of the plurality of animations as a function of the educational response. “Virtual avatar” as used in this disclosure is defined as an interactive character/entity in a virtual world. For example, a virtual avatar may include a base image consisting of a computer-generated image associated with the user/entity. “Animation” as used in this disclosure is defined as a form of digital medica production that includes using computer software to create moving images. For example, an avatar may be a 3 dimensional model that is capable of changing its shape with animations, such as human simulation animations like walking or falling down. The animation may also include video clips and animated clips, such as short videos used on a website, which may be a part of a longer recording. For example, the animation may be stitched together into sequences by splicing together multiple animations (e.g., short videos) to create a new, original video/animation. In an embodiment, there may be one or more post-sequence static set ups for the virtual avatar which may be still or in video format. For example, a virtual avatar may have a resting, default face (e.g., not showing any sign of emotion) and an expression corresponding to a previous sequence may be added. For example, a virtual avatar may initially present with a resting, default face devoid of emotion and a smiling, happy expression corresponding to a previous sequence may be added. In yet another embodiment, each sequence may include a label representing each sequence to which responses and/or contexts could be matched. In an embodiment, instantiating the virtual avatar 128 may further include generating a plurality of rules linking educational responses to animations. This may be accomplished by generating the educational model 124, which includes generating a plurality of responses and a plurality of rules matching inputs to responses (i.e. the response generated based on the input) and then generating pairs of all potential responses to animations or rules associating groups of responses to animations. For example, an education response may be positive which may be linked to an animation of hands clapping. In another embodiment, generating a plurality of rules may also include receiving a plurality of training examples correlating educational response data to animations and training a classifier using the plurality of training examples, wherein the response classifier is configured to input an educational response and output a rule linking the educational response to an animation. The response classifier machine learning model may be supervised and trained with training data. Training data may include inputs and corresponding predetermined outputs so that a machine-learning module may use the correlations between the provided exemplary inputs and outputs to develop an algorithm and/or relationship that then allows the machine-learning module to determine its own outputs for inputs. Training data may contain correlations that a machine-learning process may use to model relationships between two or more categories of data elements. The exemplary inputs and outputs may come from a database, such as any database described in this disclosure, or be provided by a user. In other embodiments, a machine-learning module may obtain a training set by querying a communicatively connected database that includes past inputs and outputs. Training data may include inputs from various types of databases, resources, and/or user inputs and outputs correlated to each of those inputs so that a machine-learning module may determine an output, such as labels of animations or sequences of labels, for an input, such as response from a chatbot, user inputs and/or a combination of one or more inputs with one or more inputs from previous iterations. For example, labels of animations or sequences of labels may be utilized to retrieve and display a animations or sequences of animations. By way of a further example, the way in which the virtual avatar responds may be based on the context of an earlier conversation and/or an earlier exchange in a conversation. Correlations may indicate causative and/or predictive links between data, which may be modeled as relationships, such as mathematical relationships, by machine-learning processes, as described in further detail below. In an embodiment, various classifiers may be utilized depending on where a geographic location of a user, the time of day and/or other circumstances. Alternatively or additionally the geographic location of a user, the time of day and/or other circumstances may be utilized as training data for the classifier. In an embodiment, the geographic location of a user, the time of day and/or other circumstances may be utilized as inputs for the classifier. Each of the above steps may be performed by classifier or other machine learning model which are described in further detail below.
In an embodiment, virtual avatar 128 may be customizable. For example, the user may be able to cosmetically design an avatar and choose personalized characteristics. Virtual avatar 128 may include, without limitation, an animal, human, robot, inanimate object, and the like. In an embodiment, personalized characteristics may be also derived from user's behavior. For example, user may have a unique gait which may be incorporated by the virtual avatar. Virtual avatar 128 may include one or more animation files and/or video clips and may include one or more files and/or video clips of the user. In an embodiment, generating the virtual avatar 128 may include creating a digital representative for simulating one or more interactions in the extended reality space. Extended reality is discussed in more detail below.
Still referring to FIG. 1, apparatus 100 may generate a Non-Fungible Token (NFT) 132 linked to the entity data 120 and the virtual avatar 128. “Non-Fungible Token (NFT)” as used in this disclosure is defined as a unique digital identifier that cannot be copied, substituted, or subdivided, that is recorded in a blockchain, and that is used to certify authenticity and ownership of an asset like digital artwork. For example, a NFT can represent an avatar, often in a cartoonish and pixelated design, which are sometimes used as profile pictures for web 2.0 and web 3.0 social media platforms. “Web 2.0” as used in this disclosure is defined as the current state of the internet, which has more user-generated content and usability for end-users compared to its earlier incarnation, Web 1.0. “Web 3.0” as used in this disclosure is defined as the third generation of the evolution of web technologies and has a strong emphasis on decentralized applications and makes extensive use of blockchain-based technologies. Web 3.0 may also make use of machine learning and artificial intelligence (AI) to help empower more intelligent and adaptive applications. In an embodiment, the Non-Fungible Token is indexed to the virtual avatar 124 and entity data 120 in a data store such as a database, knowledge base, and the like. In another embodiment, it is possible to purchase a NFT avatar with any cryptocurrency in a crypto wallet, such as Ethereum or Binance. “Cryptocurrency” as used in this disclosure is defined as a digital currency designed to work as a medium of exchange through a computer network that is not reliant on any central authority, such as a government or bank, to uphold or maintain it.
Still referring to FIG. 1, apparatus 100 may generate the virtual avatar 124 and/or NFT 132 in an extended reality space, such as, without limitation, augmented reality (AR) space, virtual reality (VR) space, and/or any other digital realities. For example, extended reality space may include a virtual classroom, virtual meeting room, virtual study room, and the like thereof. “Augmented reality” as used in this disclosure may be defined as an interactive experience that combines the real world and computer-generated content. The content can span multiple sensory modalities, including visual, auditory, haptic, somatosensory and olfactory. For example, an application of augmented reality in education includes teaching global perspectives through virtual field trips, enabling students to interactively engage with other cultures. By way of another example, students who struggle with geometry may utilize augmented reality to see and manipulate 3-dimensional geometric forms. “Virtual reality” as used in this disclosure is defined as a simulated experience that employs pose tracking and 3D near-eye displays to give the user an immersive feel of a virtual world. For example, instead of merely watching a documentary on whales, students may experience what it would be like to be a scuba diver in the water with the whales, or even, what it would be like to be a whale itself. Virtual reality may stimulate many senses usually through the use of a headset that shows 3-D graphics of the subject matter. Other senses may be stimulated such as touch, hearing, smell and the like. In an embodiment, generating the virtual avatar 128 may include utilizing computer vision. “Computer vision” as used in this disclosure is defined as a field of artificial intelligence (AI) enabling computers to derive information from images, videos and other inputs.
In an embodiment, apparatus 100 may be configured to generate virtual avatar 128 by utilizing a virtual avatar model. “Virtual avatar model” as used in this disclosure is defined as a component configured to generate a virtual avatar associated with the entity utilizing a machine learning model. In an embodiment, virtual avatar model may include 3D modeling and/or animation software. In another embodiment, virtual avatar model may be hosted in a decentralized platform (i.e., web 3.0). Virtual avatar 128 may be made of a “plurality of feature elements”, which are, as used in this disclosure, diverse types of features from a virtual avatar such as image features, frame features, sound features, graphical features, and the like. After collecting entity data 120, computing device 104 may generate a virtual avatar 128 using the virtual avatar model by utilizing virtual avatar training data comprising information from a plurality of pre-existing virtual avatars from a virtual avatar database. Virtual avatar training data may include a machine-learning training algorithm. Machine learning algorithms may include unsupervised machine learning algorithms such as clustering models, k-means clustering, hierarchical clustering, anomaly detection, local outlier factor, neural networks and the like. Machine-learning may include supervised machine learning algorithms using virtual avatar training data. Machine-learning algorithms may include lazy-learning. Machine-learning algorithm may include one or more neural networks such as convolutional and/or deep learning networks and are discussed more herein with reference to FIG. 4. To generate the virtual avatar, computing device 104 extracts a plurality of user data items from the user data. In an embodiment, apparatus 100 may create realistic virtual avatars to interact with students in real time, 24 hours a day. For example, a learner/student may ask the virtual avatar 128 a question and the virtual avatar 128 will answer in real time. The use of dynamic one on one tutoring between the virtual avatar 128 and student may be enabled.
In an embodiment, the generation of a virtual avatar 128 may include training, using the plurality of user data items and virtual avatar training data, a machine-learning model. Training data, as used in this disclosure, is data containing correlations that a machine-learning process may use to model relationships between two or more categories of data elements. For instance, and without limitation, training data may include a plurality of data entries, each entry representing a set of data elements that were recorded, received, and/or generated together; data elements may be correlated by shared existence in a given data entry, by proximity in a given data entry, or the like. Multiple data entries in training data may evince one or more trends in correlations between categories of data elements; for instance, and without limitation, a higher value of a first data element belonging to a first category of data element may tend to correlate to a higher value of a second data element belonging to a second category of data element, indicating a possible proportional or other mathematical relationship linking values belonging to the two categories. Multiple categories of data elements may be related in training data according to various correlations; correlations may indicate causative and/or predictive links between categories of data elements, which may be modeled as relationships such as mathematical relationships by machine-learning processes as described in further detail below. Training data may be formatted and/or organized by categories of data elements, for instance by associating data elements with one or more descriptors corresponding to categories of data elements. As a non-limiting example, training data may include data entered in standardized forms by persons or processes, such that entry of a given data element in a given field in a form may be mapped to one or more descriptors of categories. Elements in training data may be linked to descriptors of categories by tags, tokens, or other data elements; for instance, and without limitation, training data may be provided in fixed-length formats, formats linking positions of data to categories such as comma-separated value (CSV) formats and/or self-describing formats such as extensible markup language (XML), enabling processes or devices to detect categories of data. Generating a virtual avatar includes training the virtual avatar model using virtual avatar training data. “Virtual avatar training data” is data collected from a plurality of past, pre-generated virtual avatars that are currently used to help classify and distinguish user data received to generate the current virtual avatar.
Alternatively or additionally, and still referring to FIG. 1, training data may include one or more elements that are not categorized; that is, training data may not be formatted or contain descriptors for some elements of data. Machine-learning algorithms and/or other processes may sort training data according to one or more categorizations using, for instance, natural language processing algorithms, tokenization, detection of correlated values in raw data and the like; categories may be generated using correlation and/or other processing algorithms. As a non-limiting example, in a corpus of text, phrases making up a number “n” of compound words, such as nouns modified by other nouns, may be identified according to a statistically significant prevalence of n-grams containing such words in a particular order; such an n-gram may be categorized as an element of language such as a “word” to be tracked similarly to single words, generating a new category as a result of statistical analysis. Similarly, in a data entry including some textual data, a person's name may be identified by reference to a list, dictionary, or other compendium of terms, permitting ad-hoc categorization by machine-learning algorithms, and/or automated association of data in the data entry with descriptors or into a given format. The ability to categorize data entries automatedly May enable the same training data to be made applicable for two or more distinct machine-learning algorithms as described in further detail below. Training data used by computing device 104 may correlate any input data as described in this disclosure to any output data as described in this disclosure.
In an embodiment, machine-learning algorithms may be implemented using techniques for development of linear regression models. Linear regression models may include ordinary least squares regression, which aims to minimize the square of the difference between predicted outcomes and actual outcomes according to an appropriate norm for measuring such a difference (e.g. a vector-space distance norm); coefficients of the resulting linear equation may be modified to improve minimization. Linear regression models may include ridge regression methods, where the function to be minimized includes the least-squares function plus term multiplying the square of each coefficient by a scalar amount to penalize large coefficients. Linear regression models may include least absolute shrinkage and selection operator (LASSO) models, in which ridge regression is combined with multiplying the least-squares term by a factor of 1 divided by double the number of samples. Linear regression models may include a multi-task lasso model wherein the norm applied in the least-squares term of the lasso model is the Frobenius norm amounting to the square root of the sum of squares of all terms. Linear regression models may include the elastic net model, a multi-task elastic net model, a least angle regression model, a LARS lasso model, an orthogonal matching pursuit model, a Bayesian regression model, a logistic regression model, a stochastic gradient descent model, a perceptron model, a passive aggressive algorithm, a robustness regression model, a Huber regression model, or any other suitable model that may occur to persons skilled in the art upon reviewing the entirety of this disclosure. Linear regression models may be generalized in an embodiment to polynomial regression models, whereby a polynomial equation (e.g. a quadratic, cubic or higher-order equation) providing a best predicted output/actual output fit is sought; similar methods to those described above may be applied to minimize error functions, as will be apparent to persons skilled in the art upon reviewing the entirety of this disclosure.
Still referring to FIG. 1, machine-learning algorithms may include, without limitation, linear discriminant analysis. Machine-learning algorithm may include quadratic discriminate analysis. Machine-learning algorithms may include kernel ridge regression. Machine-learning algorithms may include support vector machines, including without limitation support vector classification-based regression processes. Machine-learning algorithms may include stochastic gradient descent algorithms, including classification and regression algorithms based on stochastic gradient descent. Machine-learning algorithms may include nearest neighbors algorithms. Machine-learning algorithms may include Gaussian processes such as Gaussian Process Regression. Machine-learning algorithms may include cross-decomposition algorithms, including partial least squares and/or canonical correlation analysis. Machine-learning algorithms may include naïve Bayes methods. Machine-learning algorithms may include algorithms based on decision trees, such as decision tree classification or regression algorithms. Machine-learning algorithms may include ensemble methods such as bagging meta-estimator, forest of randomized tress, AdaBoost, gradient tree boosting, and/or voting classifier methods. Machine-learning algorithms may include neural net algorithms, including convolutional neural net processes.
In an embodiment, virtual avatar model may be generated using alternative or additional artificial intelligence methods, including without limitation by creating an artificial neural network, such as a convolutional neural network comprising an input layer of nodes, one or more intermediate layers, and an output layer of nodes. Connections between nodes may be created via the process of “training” the network, in which elements from a training dataset are applied to the input nodes, a suitable training algorithm (such as Levenberg-Marquardt, conjugate gradient, simulated annealing, or other algorithms) is then used to adjust the connections and weights between nodes in adjacent layers of the neural network to produce the desired values at the output nodes. This process is sometimes referred to as deep learning. This network may be trained using training data.
Still referring to FIG. 1, machine-learning algorithms may include supervised machine-learning algorithms. Supervised machine learning algorithms, as defined herein, include algorithms that receive a training set relating a number of inputs to a number of outputs, and seek to find one or more mathematical relations relating inputs to outputs, where each of the one or more mathematical relations is optimal according to some criterion specified to the algorithm using some scoring function. For instance, a supervised machine-learning process may include a scoring function representing a desired form of relationship to be detected between inputs and outputs; scoring function may, for instance, seek to maximize the probability that a given input and/or combination of elements inputs is associated with a given output to minimize the probability that a given input is not associated with a given output. Scoring function may be expressed as a risk function representing an “expected loss” of an algorithm relating inputs to outputs, where loss is computed as an error function representing a degree to which a prediction generated by the relation is incorrect when compared to a given input-output pair provided in training data. Persons skilled in the art, upon reviewing the entirety of this disclosure, will be aware of various possible variations of supervised machine learning algorithms that may be used to determine relation between inputs and outputs. Machine learning model is further described herein with reference to FIG. 4.
With continued reference to FIG. 1, supervised machine-learning processes may include classification algorithms, defined as processes whereby a computing device 104 derives, from training data, a model for sorting inputs into categories or bins of data. Classification may be performed using, without limitation, linear classifiers such as without limitation logistic regression and/or naive Bayes classifiers, nearest neighbor classifiers including without limitation k-nearest neighbors classifiers, support vector machines, decision trees, boosted trees, random forest classifiers, and/or neural network-based classifiers.
Still referring to FIG. 1, machine learning processes may include unsupervised processes. An unsupervised machine-learning process, as used herein, is a process that derives inferences in datasets without regard to labels; as a result, an unsupervised machine-learning process may be free to discover any structure, relationship, and/or correlation provided in the data. Unsupervised processes may not require a response variable; unsupervised processes may be used to find interesting patterns and/or inferences between variables, to determine a degree of correlation between two or more variables, or the like. Unsupervised machine-learning algorithms may include, without limitation, clustering algorithms and/or cluster analysis processes, such as without limitation hierarchical clustering, centroid clustering, distribution clustering, clustering using density models, subspace models, group models, graph-based models, signed graph models, neural models, or the like. Unsupervised learning may be performed by neural networks and/or deep learning protocols as described above.
Continuing to refer to FIG. 1, machine-learning processes as described in this disclosure may be used to generate machine-learning models. A machine-learning model, as used herein, is a mathematical representation of a relationship between inputs and outputs, as generated using any machine-learning process including without limitation any process as described above, and stored in memory; an input is submitted to a machine-learning model once created, which generates an output based on the relationship that was derived. For instance, and without limitation, a linear regression model, generated using a linear regression algorithm, may compute a linear combination of input data using coefficients derived during machine-learning processes to calculate an output datum. As a further non-limiting example, a machine-learning model may be generated by creating an artificial neural network, such as a convolutional neural network comprising an input layer of nodes, one or more intermediate layers, and an output layer of nodes. Connections between nodes may be created via the process of “training” the network, in which elements from a training dataset are applied to the input nodes, a suitable training algorithm (such as Levenberg-Marquardt, conjugate gradient, simulated annealing, or other algorithms) is then used to adjust the connections and weights between nodes in adjacent layers of the neural network to produce the desired values at the output nodes. This process is sometimes referred to as deep learning.
Still referring to FIG. 1, computing device 104 may be configured to generate virtual avatar model using a first feature learning algorithm and virtual avatar training data. A “feature learning algorithm,” as used herein, is a machine-learning algorithm that identifies associations between elements of data in a training data set, where particular outputs and/or inputs are not specified. For instance, and without limitation, a feature learning algorithm may detect co-occurrences of sets of user data items, as defined above, with each other and with virtual avatars. Computing device 104 may perform a feature learning algorithm by dividing user data items from a given user into various sub-combinations of such data to create user data sets as described above and evaluate which user data sets tend to co-occur with which other user data sets, and virtual avatars. In an embodiment, first feature learning algorithm may perform clustering of data; for instance, a number of clusters into which data from training data sets may be sorted using feature learning may be set as a number of virtual avatars.
Continuing refer to FIG. 1, a feature learning and/or clustering algorithm may be implemented, as a non-limiting example, using a k-means clustering algorithm. A “k-means clustering algorithm” as used in this disclosure, includes cluster analysis that partitions n observations or unclassified cluster data entries into k clusters in which each observation or unclassified cluster data entry belongs to the cluster with the nearest mean, using, for instance virtual avatar training data as described above. “Cluster analysis” as used in this disclosure, includes grouping a set of observations or data entries in way that observations or data entries in the same group or cluster are more similar to each other than to those in other groups or clusters. Cluster analysis may be performed by various cluster models that include connectivity models such as hierarchical clustering, centroid models such as k-means, distribution models such as multivariate normal distribution, density models such as density-based spatial clustering of applications with nose (DBSCAN) and ordering points to identify the clustering structure (OPTICS), subspace models such as biclustering, group models, graph-based models such as a clique, signed graph models, neural models, and the like. Cluster analysis may include hard clustering whereby each observation or unclassified cluster data entry belongs to a cluster or not. Cluster analysis may include soft clustering or fuzzy clustering whereby each observation or unclassified cluster data entry belongs to each cluster to a certain degree such as for example a likelihood of belonging to a cluster; for instance, and without limitation, a fuzzy clustering algorithm may be used to identify clustering of gene combinations with multiple disease states, and vice versa. Cluster analysis may include strict partitioning clustering whereby each observation or unclassified cluster data entry belongs to exactly one cluster. Cluster analysis may include strict partitioning clustering with outliers whereby observations or unclassified cluster data entries may belong to no cluster and may be considered outliers. Cluster analysis may include overlapping clustering whereby observations or unclassified cluster data entries may belong to more than one cluster. Cluster analysis may include hierarchical clustering whereby observations or unclassified cluster data entries that belong to a child cluster also belong to a parent cluster.
With continued reference to FIG. 1, computing device 104 may generate a k-means clustering algorithm receiving unclassified user data items and/or combinations thereof with virtual avatars as inputs and outputs a definite number of classified data entry cluster wherein the data entry clusters each contain cluster data entries. K-means algorithm may select a specific number of groups or clusters to output, identified by a variable “k.” Generating a k-means clustering algorithm includes assigning inputs containing unclassified data to a “k-group” or “k-cluster” based on feature similarity. Centroids of k-groups or k-clusters may be utilized to generate classified data entry cluster. K-means clustering algorithm may select and/or be provided “k” variable by calculating k-means clustering algorithm for a range of k values and comparing results. K-means clustering algorithm may compare results across different values of k as the mean distance between cluster data entries and cluster centroid. K-means clustering algorithm may calculate mean distance to a centroid as a function of k value, and the location of where the rate of decrease starts to sharply shift, this may be utilized to select a k value. Centroids of k-groups or k-cluster include a collection of feature values which are utilized to classify data entry clusters containing cluster data entries. K-means clustering algorithm may act to classify a given virtual avatar to one or more user data sets, enabling computing device 104 to identify user data sets correlated with virtual avatars.
With continued reference to FIG. 1, generating a k-means clustering algorithm may include generating initial estimates for k centroids which may be randomly generated or randomly selected from unclassified data input. K centroids may be utilized to define one or more clusters. K-means clustering algorithm may assign unclassified data to one or more k-centroids based on the squared Euclidean distance by first performing a data assigned step of unclassified data. K-means clustering algorithm may assign unclassified data to its nearest centroid based on the collection of centroids ci of centroids in set C. Unclassified data may be assigned to a cluster based on dist(ci, x)2, where argmin includes argument of the minimum, ci includes a collection of centroids in a set C, and dist includes standard Euclidean distance. K-means clustering module may then recompute centroids by taking mean of all cluster data entries assigned to a centroid's cluster. This may be calculated based on ci=1/|Si|ΣxiSixi. K-means clustering algorithm may continue to repeat these calculations until a stopping criterion has been satisfied such as when cluster data entries do not change clusters, the sum of the distances have been minimized, and/or some maximum number of iterations has been reached.
Still referring to FIG. 1, k-means clustering algorithm may be configured to calculate a degree of similarity index value. A “degree of similarity index value” as used in this disclosure, includes a distance measurement indicating a measurement between each data entry cluster generated by k-means clustering algorithm and a selected user data sets and virtual avatars. Degree of similarity index value may indicate how close a particular combination of virtual avatars is to being classified by k-means algorithm to a particular cluster. K-means clustering algorithm may evaluate the distances of the combination of virtual avatars to the k-number of clusters output by k-means clustering algorithm. Short distances between a virtual avatar and a cluster may indicate a higher degree of similarity between virtual avatars and a particular cluster. Longer distances between virtual avatars and a cluster may indicate a lower degree of similarity between virtual avatars and a particular cluster.
With continued reference to FIG. 1, k-means clustering algorithm selects a classified data entry cluster as a function of the degree of similarity index value. In an embodiment, k-means clustering algorithm may select a classified data entry cluster with the smallest degree of similarity index value indicating a high degree of similarity between user data sets, virtual avatars and a particular data entry cluster. Alternatively or additionally k-means clustering algorithm may select a plurality of clusters having low degree of similarity index values to user data sets, virtual avatars, indicative of greater degrees of similarity. Degree of similarity index values may be compared to a threshold number indicating a minimal degree of relatedness suitable for inclusion of user data sets, virtual avatars in a cluster, where degree of similarity indices falling under the threshold number may be included as indicative of high degrees of relatedness. The above-described illustration of feature learning using k-means clustering is included for illustrative purposes only and should not be construed as limiting potential implementation of feature learning algorithms; persons skilled in the art, upon reviewing the entirety of this disclosure, will be aware of various additional or alternative feature learning approaches that may be used consistently with this disclosure.
With continued reference to FIG. 1, virtual avatar training data may be stored in and/or retrieved from one or more databases; for instance, a virtual avatar training set may be stored in and/or retrieved from a virtual avatar training database. Virtual avatar training database may include any data structure suitable for use as vice database as described above. Data entries in a virtual avatar training database may be flagged with or linked to one or more additional elements of information, which may be reflected in data entry cells and/or in linked tables such as tables related by one or more indices in a relational database. Persons skilled in the art, upon reviewing the entirety of this disclosure, will be aware of various ways in which data entries in a user database may reflect categories of data consistently with this disclosure. Virtual avatar training database may include one or more tables from which data records may be retrieved with linking data.
In an embodiment, educational model 124 may generate a virtual educational avatar using the processing steps described above. “Virtual educational avatar” as used in this disclosure is defined as a proxy agent configured to receive the educational data from the educational model and output the educational data to the at least one virtual avatar in a visual and/or audible way. For example, virtual education avatar may be a virtual teacher. For example, an education lecture may be provided by the virtual education avatar which may include displaying the image of the virtual education avatar on a display screen (e.g., simulating the appearance of an actual teacher) speaking about a subject using the virtual education avatar's voice data via a text-to-speech function or the like. In an embodiment, the virtual educational avatar may also be able to interact with the student/learner by providing questions and/or answering questions from the student/learner in real time. The content of the information exchanged between the virtual educational avatar and the student/learner may be utilized as training data for the virtual educational avatar. The content of the student/learner answers may be stored as a determination of the student's academic level, comprehension and the like. Virtual educational avatar may be generated in same extended reality space as the virtual avatar 124. For example, extended reality space may include a virtual classroom in virtual reality space.
Referring now to FIG. 2, an exemplary embodiment 200 of an educational data database is illustrated. Educational data database may be implemented as any data structure suitable for use as clustering database. Educational data database may store subject content data 204. Subject content data may include educational information related to various subjects such as, without limitation, math, science, English, social studies and the like. Educational data database may also store subject level data 208. Subject level data 208 may include information related to the level of the subject, for example, beginner level math or advanced level math.
Now referring to FIG. 3, an exemplary embodiment of an immutable sequential listing 300 is illustrated. Data elements may be listed in an immutable sequential listing 300; data elements may include any form of data, including textual data, image data, encrypted data, cryptographically hashed data, and the like. Data elements may include, without limitation, one or more at least a digitally signed assertions. In one embodiment, a digitally signed assertion 304 is a collection of textual data signed using a secure proof as described in further detail below; secure proof may include, without limitation, a digital signature as described above. Collection of textual data may contain any textual data, including without limitation American Standard Code for Information Interchange (ASCII), Unicode, or similar computer-encoded textual data, any alphanumeric data, punctuation, diacritical mark, or any character or other marking used in any writing system to convey information, in any form, including any plaintext or cyphertext data; in an embodiment, collection of textual data may be encrypted, or may be a hash of other data, such as a root or node of a Merkle tree or hash tree, or a hash of any other information desired to be recorded in some fashion using a digitally signed assertion 304. In an embodiment, collection of textual data states that the owner of a certain transferable item represented in a digitally signed assertion 304 register is transferring that item to the owner of an address. A digitally signed assertion 304 may be signed by a digital signature created using the private key associated with the owner's public key, as described above.
Still referring to FIG. 3, a digitally signed assertion 304 may describe a transfer of virtual currency, such as crypto-currency as described below. The virtual currency may be a digital currency. Item of value may be a transfer of trust, for instance represented by a statement vouching for the identity or trustworthiness of the first entity. Item of value may be an interest in a fungible negotiable financial instrument representing ownership in a public or private corporation, a creditor relationship with a governmental body or a corporation, rights to ownership represented by an option, derivative financial instrument, commodity, debt-backed security such as a bond or debenture or other security as described in further detail below. A resource may be a physical machine e.g. a ride share vehicle or any other asset. A digitally signed assertion 304 may describe the transfer of a physical good; for instance, a digitally signed assertion 304 may describe the sale of a product. In some embodiments, a transfer nominally of one item may be used to represent a transfer of another item; for instance, a transfer of virtual currency may be interpreted as representing a transfer of an access right; conversely, where the item nominally transferred is something other than virtual currency, the transfer itself may still be treated as a transfer of virtual currency, having value that depends on many potential factors including the value of the item nominally transferred and the monetary value attendant to having the output of the transfer moved into a particular user's control. The item of value may be associated with a digitally signed assertion 304 by means of an exterior protocol, such as the COLORED COINS created according to protocols developed by The Colored Coins Foundation, the MASTERCOIN protocol developed by the Mastercoin Foundation, or the ETHEREUM platform offered by the Stiftung Ethereum Foundation of Baar, Switzerland, the Thunder protocol developed by Thunder Consensus, or any other protocol.
Still referring to FIG. 3, in one embodiment, an address is a textual datum identifying the recipient of virtual currency or another item of value in a digitally signed assertion 304. In some embodiments, address is linked to a public key, the corresponding private key of which is owned by the recipient of a digitally signed assertion 304. For instance, address may be the public key. Address may be a representation, such as a hash, of the public key. Address may be linked to the public key in memory of a computing device, for instance via a “wallet shortener” protocol. Where address is linked to a public key, a transferee in a digitally signed assertion 304 may record a subsequent a digitally signed assertion 304 transferring some or all of the value transferred in the first a digitally signed assertion 304 to a new address in the same manner. A digitally signed assertion 304 may contain textual information that is not a transfer of some item of value in addition to, or as an alternative to, such a transfer. For instance, as described in further detail below, a digitally signed assertion 304 may indicate a confidence level associated with a distributed storage node as described in further detail below.
In an embodiment, and still referring to FIG. 3 immutable sequential listing 300 records a series of the communication of educational data. Temporally sequential listing may be accessible at any of various security settings; for instance, and without limitation, temporally sequential listing may be readable and modifiable publicly, may be publicly readable but writable only by entities and/or devices having access privileges established by password protection, confidence level, or any device authentication procedure or facilities described herein, or may be readable and/or writable only by entities and/or devices having such access privileges. Access privileges may exist in more than one level, including, without limitation, a first access level or community of permitted entities and/or devices having ability to read, and a second access level or community of permitted entities and/or devices having ability to write; first and second community may be overlapping or non-overlapping. In an embodiment, posted content and/or immutable sequential listing 300 may be stored as one or more zero knowledge sets (ZKS), Private Information Retrieval (PIR) structure, or any other structure that allows checking of membership in a set by querying with specific properties. Such database may incorporate protective measures to ensure that malicious actors may not query the database repeatedly in an effort to narrow the members of a set to reveal uniquely identifying information of a given posted content.
Still referring to FIG. 3, immutable sequential listing 300 may preserve the order in which communication of educational data took place by listing it in chronological order; alternatively or additionally, immutable sequential listing 300 may organize digitally signed assertions 304 into sub-listings 308 such as “blocks” in a blockchain, which may be themselves collected in a temporally sequential order; digitally signed assertions 304 within a sub-listing 308 may or may not be temporally sequential. The ledger may preserve the order in which at least a posted content took place by listing them in sub-listings 308 and placing the sub-listings 308 in chronological order. The immutable sequential listing 300 may be a distributed, consensus-based ledger, such as those operated according to the protocols promulgated by Ripple Labs, Inc., of San Francisco, Calif., or the Stellar Development Foundation, of San Francisco, Calif, or of Thunder Consensus. In some embodiments, the ledger is a secured ledger; in one embodiment, a secured ledger is a ledger having safeguards against alteration by unauthorized parties. The ledger may be maintained by a proprietor, such as a system administrator on a server, that controls access to the ledger; for instance, the user account controls may allow contributors to the ledger to add at least a posted content to the ledger, but may not allow any users to alter at least a posted content that have been added to the ledger. In some embodiments, ledger is cryptographically secured; in one embodiment, a ledger is cryptographically secured where each link in the chain contains encrypted or hashed information that makes it practically infeasible to alter the ledger without betraying that alteration has taken place, for instance by requiring that an administrator or other party sign new additions to the chain with a digital signature. Immutable sequential listing 300 may be incorporated in, stored in, or incorporate, any suitable data structure, including without limitation any database, datastore, file structure, distributed hash table, directed acyclic graph or the like. In some embodiments, the timestamp of an entry is cryptographically secured and validated via trusted time, either directly on the chain or indirectly by utilizing a separate chain. In one embodiment the validity of timestamp is provided using a time stamping authority as described in the RFC 3161 standard for trusted timestamps, or in the ANSI ASC x9.95 standard. In another embodiment, the trusted time ordering is provided by a group of entities collectively acting as the time stamping authority with a requirement that a threshold number of the group of authorities sign the timestamp.
In some embodiments, and with continued reference to FIG. 3, immutable sequential listing 300, once formed, may be inalterable by any party, no matter what access rights that party possesses. For instance, immutable sequential listing 300 may include a hash chain, in which data is added during a successive hashing process to ensure non-repudiation. Immutable sequential listing 300 may include a block chain. In one embodiment, a block chain is immutable sequential listing 300 that records one or more new at least a posted content in a data item known as a sub-listing 308 or “block.” An example of a block chain is the BITCOIN block chain used to record BITCOIN transactions and values. Sub-listings 308 may be created in a way that places the sub-listings 308 in chronological order and link each sub-listing 308 to a previous sub-listing 308 in the chronological order so that any computing device may traverse the sub-listings 308 in reverse chronological order to verify any at least a posted content listed in the block chain. Each new sub-listing 308 may be required to contain a cryptographic hash describing the previous sub-listing 308. In some embodiments, the block chain contains a single first sub-listing 308 sometimes known as a “genesis block.”
Still referring to FIG. 3, the creation of a new sub-listing 308 may be computationally expensive; for instance, the creation of a new sub-listing 308 may be designed by a “proof of work” protocol accepted by all participants in forming the immutable sequential listing 300 to take a powerful set of computing devices a certain period of time to produce. Where one sub-listing 308 takes less time for a given set of computing devices to produce the sub-listing 308 protocol may adjust the algorithm to produce the next sub-listing 308 so that it will require more steps; where one sub-listing 308 takes more time for a given set of computing devices to produce the sub-listing 308 protocol may adjust the algorithm to produce the next sub-listing 308 so that it will require fewer steps. As an example, protocol may require a new sub-listing 308 to contain a cryptographic hash describing its contents; the cryptographic hash may be required to satisfy a mathematical condition, achieved by having the sub-listing 308 contain a number, called a nonce, whose value is determined after the fact by the discovery of the hash that satisfies the mathematical condition. Continuing the example, the protocol may be able to adjust the mathematical condition so that the discovery of the hash describing a sub-listing 308 and satisfying the mathematical condition requires more or less steps, depending on the outcome of the previous hashing attempt. Mathematical condition, as an example, might be that the hash contains a certain number of leading zeros and a hashing algorithm that requires more steps to find a hash containing a greater number of leading zeros, and fewer steps to find a hash containing a lesser number of leading zeros. In some embodiments, production of a new sub-listing 308 according to the protocol is known as “mining.” The creation of a new sub-listing 308 may be designed by a “proof of stake” protocol as will be apparent to those skilled in the art upon reviewing the entirety of this disclosure.
Continuing to refer to FIG. 3, in some embodiments, protocol also creates an incentive to mine new sub-listings 308. The incentive may be financial; for instance, successfully mining a new sub-listing 308 may result in the person or entity that mines the sub-listing 308 receiving a predetermined amount of currency. The currency may be fiat currency. Currency may be cryptocurrency as defined below. In other embodiments, incentive may be redeemed for particular products or services; the incentive may be a gift certificate with a particular business, for instance. In some embodiments, incentive is sufficiently attractive to cause participants to compete for the incentive by trying to race each other to the creation of sub-listings 308 Each sub-listing 308 created in immutable sequential listing 300 may contain a record or at least a posted content describing one or more addresses that receive an incentive, such as virtual currency, as the result of successfully mining the sub-listing 308.
With continued reference to FIG. 3, where two entities simultaneously create new sub-listings 308, immutable sequential listing 300 may develop a fork; protocol may determine which of the two alternate branches in the fork is the valid new portion of the immutable sequential listing 300 by evaluating, after a certain amount of time has passed, which branch is longer. “Length” may be measured according to the number of sub-listings 308 in the branch. Length may be measured according to the total computational cost of producing the branch. Protocol may treat only at least a posted content contained the valid branch as valid at least a posted content. When a branch is found invalid according to this protocol, at least a posted content registered in that branch may be recreated in a new sub-listing 308 in the valid branch; the protocol may reject “double spending” at least a posted content that transfer the same virtual currency that another at least a posted content in the valid branch has already transferred. As a result, in some embodiments the creation of fraudulent at least a posted content requires the creation of a longer immutable sequential listing 300 branch by the entity attempting the fraudulent at least a posted content than the branch being produced by the rest of the participants; as long as the entity creating the fraudulent at least a posted content is likely the only one with the incentive to create the branch containing the fraudulent at least a posted content, the computational cost of the creation of that branch may be practically infeasible, guaranteeing the validity of all at least a posted content in the immutable sequential listing 300.
Still referring to FIG. 3, additional data linked to at least a posted content may be incorporated in sub-listings 308 in the immutable sequential listing 300; for instance, data may be incorporated in one or more fields recognized by block chain protocols that permit a person or computer forming a at least a posted content to insert additional data in the immutable sequential listing 300. In some embodiments, additional data is incorporated in an unspendable at least a posted content field. For instance, the data may be incorporated in an OP_RETURN within the BITCOIN block chain. In other embodiments, additional data is incorporated in one signature of a multi-signature at least a posted content. In an embodiment, a multi-signature at least a posted content is at least a posted content to two or more addresses. In some embodiments, the two or more addresses are hashed together to form a single address, which is signed in the digital signature of the at least a posted content. In other embodiments, the two or more addresses are concatenated. In some embodiments, two or more addresses may be combined by a more complicated process, such as the creation of a Merkle tree or the like. In some embodiments, one or more addresses incorporated in the multi-signature at least a posted content are typical crypto-currency addresses, such as addresses linked to public keys as described above, while one or more additional addresses in the multi-signature at least a posted content contain additional data related to the at least a posted content; for instance, the additional data may indicate the purpose of the at least a posted content, aside from an exchange of virtual currency, such as the item for which the virtual currency was exchanged. In some embodiments, additional information may include network statistics for a given node of network, such as a distributed storage node, e.g. the latencies to nearest neighbors in a network graph, the identities or identifying information of neighboring nodes in the network graph, the trust level and/or mechanisms of trust (e.g. certificates of physical encryption keys, certificates of software encryption keys, (in non-limiting example certificates of software encryption may indicate the firmware version, manufacturer, hardware version and the like), certificates from a trusted third party, certificates from a decentralized anonymous authentication procedure, and other information quantifying the trusted status of the distributed storage node) of neighboring nodes in the network graph, IP addresses, GPS coordinates, and other information informing location of the node and/or neighboring nodes, geographically and/or within the network graph. In some embodiments, additional information may include history and/or statistics of neighboring nodes with which the node has interacted. In some embodiments, this additional information may be encoded directly, via a hash, hash tree or other encoding.
With continued reference to FIG. 3, in some embodiments, virtual currency is traded as a crypto currency. In one embodiment, a crypto currency is a digital, currency such as Bitcoins, Peercoins, Namecoins, and Litecoins. Crypto-currency may be a clone of another crypto-currency. The crypto-currency may be an “alt-coin.” Crypto-currency may be decentralized, with no particular entity controlling it; the integrity of the crypto-currency may be maintained by adherence by its participants to established protocols for exchange and for production of new currency, which may be enforced by software implementing the crypto-currency. Crypto-currency may be centralized, with its protocols enforced or hosted by a particular entity. For instance, crypto-currency may be maintained in a centralized ledger, as in the case of the XRP currency of Ripple Labs, Inc., of San Francisco, Calif. In lieu of a centrally controlling authority, such as a national bank, to manage currency values, the number of units of a particular crypto-currency may be limited; the rate at which units of crypto-currency enter the market may be managed by a mutually agreed-upon process, such as creating new units of currency when mathematical puzzles are solved, the degree of difficulty of the puzzles being adjustable to control the rate at which new units enter the market. Mathematical puzzles may be the same as the algorithms used to make productions of sub-listings 308 in a block chain computationally challenging; the incentive for producing sub-listings 308 may include the grant of new crypto currency to the miners. Quantities of crypto-currency may be exchanged using at least a posted content as described above.
Referring now to FIG. 4, an exemplary embodiment of a machine-learning module 400 that may perform one or more machine-learning processes as described in this disclosure is illustrated. Machine-learning module may perform determinations, classification, and/or analysis steps, methods, processes, or the like as described in this disclosure using machine learning processes. A “machine learning process,” as used in this disclosure, is a process that automatedly uses training data 404 to generate an algorithm that will be performed by a computing device/module to produce outputs 408 given data provided as inputs 412; this is in contrast to a non-machine learning software program where the commands to be executed are determined in advance by a user and written in a programming language.
Still referring to FIG. 4, “training data,” as used herein, is data containing correlations that a machine-learning process may use to model relationships between two or more categories of data elements. For instance, and without limitation, training data 404 may include a plurality of data entries, each entry representing a set of data elements that were recorded, received, and/or generated together; data elements may be correlated by shared existence in a given data entry, by proximity in a given data entry, or the like. Multiple data entries in training data 404 may evince one or more trends in correlations between categories of data elements; for instance, and without limitation, a higher value of a first data element belonging to a first category of data element may tend to correlate to a higher value of a second data element belonging to a second category of data element, indicating a possible proportional or other mathematical relationship linking values belonging to the two categories. Multiple categories of data elements may be related in training data 404 according to various correlations; correlations may indicate causative and/or predictive links between categories of data elements, which may be modeled as relationships such as mathematical relationships by machine-learning processes as described in further detail below. Training data 404 may be formatted and/or organized by categories of data elements, for instance by associating data elements with one or more descriptors corresponding to categories of data elements. As a non-limiting example, training data 404 may include data entered in standardized forms by persons or processes, such that entry of a given data element in a given field in a form may be mapped to one or more descriptors of categories. Elements in training data 404 may be linked to descriptors of categories by tags, tokens, or other data elements; for instance, and without limitation, training data 404 may be provided in fixed-length formats, formats linking positions of data to categories such as comma-separated value (CSV) formats and/or self-describing formats such as extensible markup language (XML), JavaScript Object Notation (JSON), or the like, enabling processes or devices to detect categories of data.
Alternatively or additionally, and continuing to refer to FIG. 4, training data 404 may include one or more elements that are not categorized; that is, training data 404 may not be formatted or contain descriptors for some elements of data. Machine-learning algorithms and/or other processes may sort training data 404 according to one or more categorizations using, for instance, natural language processing algorithms, tokenization, detection of correlated values in raw data and the like; categories may be generated using correlation and/or other processing algorithms. As a non-limiting example, in a corpus of text, phrases making up a number “n” of compound words, such as nouns modified by other nouns, may be identified according to a statistically significant prevalence of n-grams containing such words in a particular order; such an n-gram may be categorized as an element of language such as a “word” to be tracked similarly to single words, generating a new category as a result of statistical analysis. Similarly, in a data entry including some textual data, a person's name may be identified by reference to a list, dictionary, or other compendium of terms, permitting ad-hoc categorization by machine-learning algorithms, and/or automated association of data in the data entry with descriptors or into a given format. The ability to categorize data entries automatedly may enable the same training data 404 to be made applicable for two or more distinct machine-learning algorithms as described in further detail below. Training data 404 used by machine-learning module 400 may correlate any input data as described in this disclosure to any output data as described in this disclosure. As a non-limiting illustrative example, inputs of the current disclosure include user data items, while outputs include virtual avatars.
Further referring to FIG. 4, training data may be filtered, sorted, and/or selected using one or more supervised and/or unsupervised machine-learning processes and/or models as described in further detail below; such models may include without limitation a training data classifier 416. Training data classifier 416 may include a “classifier,” which as used in this disclosure is a machine-learning model as defined below, such as a mathematical model, neural net, or program generated by a machine learning algorithm known as a “classification algorithm,” as described in further detail below, that sorts inputs into categories or bins of data, outputting the categories or bins of data and/or labels associated therewith. A classifier may be configured to output at least a datum that labels or otherwise identifies a set of data that are clustered together, found to be close under a distance metric as described below, or the like. Machine-learning module 400 may generate a classifier using a classification algorithm, defined as a processes whereby a computing device and/or any module and/or component operating thereon derives a classifier from training data 404. Classification may be performed using, without limitation, linear classifiers such as without limitation logistic regression and/or naive Bayes classifiers, nearest neighbor classifiers such as k-nearest neighbors classifiers, support vector machines, least squares support vector machines, fisher's linear discriminant, quadratic classifiers, decision trees, boosted trees, random forest classifiers, learning vector quantization, and/or neural network-based classifiers. As a non-limiting example, training data classifier 416 may classify elements of training data to be associated with categories of virtual avatars.
Still referring to FIG. 4, machine-learning module 400 may be configured to perform a lazy-learning process 420 and/or protocol, which may alternatively be referred to as a “lazy loading” or “call-when-needed” process and/or protocol, may be a process whereby machine learning is conducted upon receipt of an input to be converted to an output, by combining the input and training set to derive the algorithm to be used to produce the output on demand. For instance, an initial set of simulations may be performed to cover an initial heuristic and/or “first guess” at an output and/or relationship. As a non-limiting example, an initial heuristic may include a ranking of associations between inputs and elements of training data 404. Heuristic may include selecting some number of highest-ranking associations and/or training data 404 elements. Lazy learning may implement any suitable lazy learning algorithm, including without limitation a K-nearest neighbors algorithm, a lazy naïve Bayes algorithm, or the like; persons skilled in the art, upon reviewing the entirety of this disclosure, will be aware of various lazy-learning algorithms that may be applied to generate outputs as described in this disclosure, including without limitation lazy learning applications of machine-learning algorithms as described in further detail below.
Alternatively or additionally, and with continued reference to FIG. 4, machine-learning processes as described in this disclosure may be used to generate machine-learning models 424. A “machine-learning model,” as used in this disclosure, is a mathematical and/or algorithmic representation of a relationship between inputs and outputs, as generated using any machine-learning process including without limitation any process as described above and stored in memory; an input is submitted to a machine-learning model 424 once created, which generates an output based on the relationship that was derived. For instance, and without limitation, a linear regression model, generated using a linear regression algorithm, may compute a linear combination of input data using coefficients derived during machine-learning processes to calculate an output datum. As a further non-limiting example, a machine-learning model 424 may be generated by creating an artificial neural network, such as a convolutional neural network comprising an input layer of nodes, one or more intermediate layers, and an output layer of nodes. Connections between nodes may be created via the process of “training” the network, in which elements from a training data 404 set are applied to the input nodes, a suitable training algorithm (such as Levenberg-Marquardt, conjugate gradient, simulated annealing, or other algorithms) is then used to adjust the connections and weights between nodes in adjacent layers of the neural network to produce the desired values at the output nodes. This process is sometimes referred to as deep learning.
Still referring to FIG. 4, machine-learning algorithms may include at least a supervised machine-learning process 428. At least a supervised machine-learning process 428, as defined herein, include algorithms that receive a training set relating a number of inputs to a number of outputs, and seek to find one or more mathematical relations relating inputs to outputs, where each of the one or more mathematical relations is optimal according to some criterion specified to the algorithm using some scoring function. For instance, a supervised learning algorithm may include user data items as described above as inputs, virtual avatar outputs, and a scoring function representing a desired form of relationship to be detected between inputs and outputs; scoring function may, for instance, seek to maximize the probability that a given input and/or combination of elements inputs is associated with a given output to minimize the probability that a given input is not associated with a given output. Scoring function may be expressed as a risk function representing an “expected loss” of an algorithm relating inputs to outputs, where loss is computed as an error function representing a degree to which a prediction generated by the relation is incorrect when compared to a given input-output pair provided in training data 404. Persons skilled in the art, upon reviewing the entirety of this disclosure, will be aware of various possible variations of at least a supervised machine-learning process 428 that may be used to determine relation between inputs and outputs. Supervised machine-learning processes may include classification algorithms as defined above.
Further referring to FIG. 4, machine learning processes may include at least an unsupervised machine-learning processes 432. An unsupervised machine-learning process, as used herein, is a process that derives inferences in datasets without regard to labels; as a result, an unsupervised machine-learning process may be free to discover any structure, relationship, and/or correlation provided in the data. Unsupervised processes may not require a response variable; unsupervised processes may be used to find interesting patterns and/or inferences between variables, to determine a degree of correlation between two or more variables, or the like.
Still referring to FIG. 4, machine-learning module 400 may be designed and configured to create a machine-learning model 424 using techniques for development of linear regression models. Linear regression models may include ordinary least squares regression, which aims to minimize the square of the difference between predicted outcomes and actual outcomes according to an appropriate norm for measuring such a difference (e.g. a vector-space distance norm); coefficients of the resulting linear equation may be modified to improve minimization. Linear regression models may include ridge regression methods, where the function to be minimized includes the least-squares function plus term multiplying the square of each coefficient by a scalar amount to penalize large coefficients. Linear regression models may include least absolute shrinkage and selection operator (LASSO) models, in which ridge regression is combined with multiplying the least-squares term by a factor of 1 divided by double the number of samples. Linear regression models may include a multi-task lasso model wherein the norm applied in the least-squares term of the lasso model is the Frobenius norm amounting to the square root of the sum of squares of all terms. Linear regression models may include the elastic net model, a multi-task elastic net model, a least angle regression model, a LARS lasso model, an orthogonal matching pursuit model, a Bayesian regression model, a logistic regression model, a stochastic gradient descent model, a perceptron model, a passive aggressive algorithm, a robustness regression model, a Huber regression model, or any other suitable model that may occur to persons skilled in the art upon reviewing the entirety of this disclosure. Linear regression models may be generalized in an embodiment to polynomial regression models, whereby a polynomial equation (e.g. a quadratic, cubic or higher-order equation) providing a best predicted output/actual output fit is sought; similar methods to those described above may be applied to minimize error functions, as will be apparent to persons skilled in the art upon reviewing the entirety of this disclosure.
Continuing to refer to FIG. 4, machine-learning algorithms may include, without limitation, linear discriminant analysis. Machine-learning algorithm may include quadratic discriminate analysis. Machine-learning algorithms may include kernel ridge regression. Machine-learning algorithms may include support vector machines, including without limitation support vector classification-based regression processes. Machine-learning algorithms may include stochastic gradient descent algorithms, including classification and regression algorithms based on stochastic gradient descent. Machine-learning algorithms may include nearest neighbors algorithms. Machine-learning algorithms may include various forms of latent space regularization such as variational regularization. Machine-learning algorithms may include various forms of latent space regularization such as variational regularization. Machine-learning algorithms may include Gaussian processes such as Gaussian Process Regression. Machine-learning algorithms may include cross-decomposition algorithms, including partial least squares and/or canonical correlation analysis. Machine-learning algorithms may include naïve Bayes methods. Machine-learning algorithms may include algorithms based on decision trees, such as decision tree classification or regression algorithms. Machine-learning algorithms may include ensemble methods such as bagging meta-estimator, forest of randomized tress, AdaBoost, gradient tree boosting, and/or voting classifier methods. Machine-learning algorithms may include neural net algorithms, including convolutional neural net processes.
Referring now to FIG. 5, an exemplary embodiment of a method for an education platform and ecosystem using extended reality. The method is performed by a computing device. Computing device may be any of the computing devices discussed herein with reference to FIGS. 1 and 6.
Still referring to FIG. 5, at step 505, method 500 includes receiving, by at least a processor, entity data associated with an entity. This may be implemented, as described above in reference to FIGS. 1-4.
Still referring to FIG. 5, at step 510, method 500 includes generating, by the at least a processor, an educational model, wherein the educational model is configured to receive an input from a user and generate an educational response as a function of the at least an input. The educational model may be hosted in a decentralized platform and the educational model may receive an input from a user by the use of a chatbot. A machine learning model may be utilized to generate chatbot responses as a function of answer datum as an input and output additional questions. The user responses to the chatbot are input into a chatbot input classifier creating a feedback loop. This may be implemented, as described above in reference to FIGS. 1-4.
Still referring to FIG. 5, at step 515, method 500 includes instantiating, by the at least a processor, in a user interface, a virtual avatar, wherein the virtual avatar comprises at least a base image and a plurality of animations of the base image, wherein the virtual avatar is further configured to receive an educational response and display an animation of the plurality of animations as a function of the an educational response. instantiating the virtual avatar may further include generating a plurality of rules linking educational responses to animations. Generating the plurality of rules further includes receiving a plurality of training examples correlating educational response data to animations and training a classifier using the plurality of training examples, wherein the classifier is configured to input an educational response and output a rule linking the educational response to an animation. This may be implemented, as described above in reference to FIGS. 1-4.
Still referring to FIG. 5, at step 520, method 500 includes generating, by the processor, a non-fungible token linked to the entity data and the virtual avatar. The virtual avatar data and entity data may be utilized to generate the non-fungible token. The non-fungible token may be indexed to the virtual avatar and entity data in a data store. This may be implemented, as described above in reference to FIGS. 1-4.
It is to be noted that any one or more of the aspects and embodiments described herein may be conveniently implemented using one or more machines (e.g., one or more computing devices that are utilized as a user computing device for an electronic document, one or more server devices, such as a document server, etc.) programmed according to the teachings of the present specification, as will be apparent to those of ordinary skill in the computer art. Appropriate software coding can readily be prepared by skilled programmers based on the teachings of the present disclosure, as will be apparent to those of ordinary skill in the software art. Aspects and implementations discussed above employing software and/or software modules may also include appropriate hardware for assisting in the implementation of the machine executable instructions of the software and/or software module.
Such software may be a computer program product that employs a machine-readable storage medium. A machine-readable storage medium may be any medium that is capable of storing and/or encoding a sequence of instructions for execution by a machine (e.g., a computing device) and that causes the machine to perform any one of the methodologies and/or embodiments described herein. Examples of a machine-readable storage medium include, but are not limited to, a magnetic disk, an optical disc (e.g., CD, CD-R, DVD, DVD-R, etc.), a magneto-optical disk, a read-only memory “ROM” device, a random access memory “RAM” device, a magnetic card, an optical card, a solid-state memory device, an EPROM, an EEPROM, and any combinations thereof. A machine-readable medium, as used herein, is intended to include a single medium as well as a collection of physically separate media, such as, for example, a collection of compact discs or one or more hard disk drives in combination with a computer memory. As used herein, a machine-readable storage medium does not include transitory forms of signal transmission.
Such software may also include information (e.g., data) carried as a data signal on a data carrier, such as a carrier wave. For example, machine-executable information may be included as a data-carrying signal embodied in a data carrier in which the signal encodes a sequence of instruction, or portion thereof, for execution by a machine (e.g., a computing device) and any related information (e.g., data structures and data) that causes the machine to perform any one of the methodologies and/or embodiments described herein.
Examples of a computing device include, but are not limited to, an electronic book reading device, a computer workstation, a terminal computer, a server computer, a handheld device (e.g., a tablet computer, a smartphone, etc.), a web appliance, a network router, a network switch, a network bridge, any machine capable of executing a sequence of instructions that specify an action to be taken by that machine, and any combinations thereof. In one example, a computing device may include and/or be included in a kiosk.
FIG. 6 shows a diagrammatic representation of one embodiment of a computing device in the exemplary form of a computer system 600 within which a set of instructions for causing a control system to perform any one or more of the aspects and/or methodologies of the present disclosure may be executed. It is also contemplated that multiple computing devices may be utilized to implement a specially configured set of instructions for causing one or more of the devices to perform any one or more of the aspects and/or methodologies of the present disclosure. Computer system 600 includes a processor 604 and a memory 608 that communicate with each other, and with other components, via a bus 612. Bus 612 may include any of several types of bus structures including, but not limited to, a memory bus, a memory controller, a peripheral bus, a local bus, and any combinations thereof, using any of a variety of bus architectures.
Processor 604 may include any suitable processor, such as without limitation a processor incorporating logical circuitry for performing arithmetic and logical operations, such as an arithmetic and logic unit (ALU), which may be regulated with a state machine and directed by operational inputs from memory and/or sensors; processor 604 may be organized according to Von Neumann and/or Harvard architecture as a non-limiting example. Processor 604 may include, incorporate, and/or be incorporated in, without limitation, a microcontroller, microprocessor, digital signal processor (DSP), Field Programmable Gate Array (FPGA), Complex Programmable Logic Device (CPLD), Graphical Processing Unit (GPU), general purpose GPU, Tensor Processing Unit (TPU), analog or mixed signal processor, Trusted Platform Module (TPM), a floating point unit (FPU), and/or system on a chip (SoC).
Memory 608 may include various components (e.g., machine-readable media) including, but not limited to, a random-access memory component, a read only component, and any combinations thereof. In one example, a basic input/output system 616 (BIOS), including basic routines that help to transfer information between elements within computer system 600, such as during start-up, may be stored in memory 608. Memory 608 may also include (e.g., stored on one or more machine-readable media) instructions (e.g., software) 620 embodying any one or more of the aspects and/or methodologies of the present disclosure. In another example, memory 608 may further include any number of program modules including, but not limited to, an operating system, one or more application programs, other program modules, program data, and any combinations thereof.
Computer system 600 may also include a storage device 624. Examples of a storage device (e.g., storage device 624) include, but are not limited to, a hard disk drive, a magnetic disk drive, an optical disc drive in combination with an optical medium, a solid-state memory device, and any combinations thereof. Storage device 624 may be connected to bus 612 by an appropriate interface (not shown). Example interfaces include, but are not limited to, SCSI, advanced technology attachment (ATA), serial ATA, universal serial bus (USB), IEEE 1394 (FIREWIRE), and any combinations thereof. In one example, storage device 624 (or one or more components thereof) may be removably interfaced with computer system 600 (e.g., via an external port connector (not shown)). Particularly, storage device 624 and an associated machine-readable medium 628 may provide nonvolatile and/or volatile storage of machine-readable instructions, data structures, program modules, and/or other data for computer system 600. In one example, software 620 may reside, completely or partially, within machine-readable medium 628. In another example, software 620 may reside, completely or partially, within processor 604.
Computer system 600 may also include an input device 632. In one example, a user of computer system 600 may enter commands and/or other information into computer system 600 via input device 632. Examples of an input device 632 include, but are not limited to, an alpha-numeric input device (e.g., a keyboard), a pointing device, a joystick, a gamepad, an audio input device (e.g., a microphone, a voice response system, etc.), a cursor control device (e.g., a mouse), a touchpad, an optical scanner, a video capture device (e.g., a still camera, a video camera), a touchscreen, and any combinations thereof. Input device 632 may be interfaced to bus 612 via any of a variety of interfaces (not shown) including, but not limited to, a serial interface, a parallel interface, a game port, a USB interface, a FIREWIRE interface, a direct interface to bus 612, and any combinations thereof. Input device 632 may include a touch screen interface that may be a part of or separate from display 636, discussed further below. Input device 632 may be utilized as a user selection device for selecting one or more graphical representations in a graphical interface as described above.
A user may also input commands and/or other information to computer system 600 via storage device 624 (e.g., a removable disk drive, a flash drive, etc.) and/or network interface device 640. A network interface device, such as network interface device 640, may be utilized for connecting computer system 600 to one or more of a variety of networks, such as network 644, and one or more remote devices 648 connected thereto. Examples of a network interface device include, but are not limited to, a network interface card (e.g., a mobile network interface card, a LAN card), a modem, and any combination thereof. Examples of a network include, but are not limited to, a wide area network (e.g., the Internet, an enterprise network), a local area network (e.g., a network associated with an office, a building, a campus or other relatively small geographic space), a telephone network, a data network associated with a telephone/voice provider (e.g., a mobile communications provider data and/or voice network), a direct connection between two computing devices, and any combinations thereof. A network, such as network 644, may employ a wired and/or a wireless mode of communication. In general, any network topology may be used. Information (e.g., data, software 620, etc.) may be communicated to and/or from computer system 600 via network interface device 640.
Computer system 600 may further include a video display adapter 652 for communicating a displayable image to a display device, such as display device 636. Examples of a display device include, but are not limited to, a liquid crystal display (LCD), a cathode ray tube (CRT), a plasma display, a light emitting diode (LED) display, and any combinations thereof. Display adapter 652 and display device 636 may be utilized in combination with processor 604 to provide graphical representations of aspects of the present disclosure. In addition to a display device, computer system 600 may include one or more other peripheral output devices including, but not limited to, an audio speaker, a printer, and any combinations thereof. Such peripheral output devices may be connected to bus 612 via a peripheral interface 656. Examples of a peripheral interface include, but are not limited to, a serial port, a USB connection, a FIREWIRE connection, a parallel connection, and any combinations thereof.
The foregoing has been a detailed description of illustrative embodiments of the invention. Various modifications and additions can be made without departing from the spirit and scope of this invention. Features of each of the various embodiments described above may be combined with features of other described embodiments as appropriate in order to provide a multiplicity of feature combinations in associated new embodiments. Furthermore, while the foregoing describes a number of separate embodiments, what has been described herein is merely illustrative of the application of the principles of the present invention. Additionally, although particular methods herein may be illustrated and/or described as being performed in a specific order, the ordering is highly variable within ordinary skill to achieve methods, systems, and software according to the present disclosure. Accordingly, this description is meant to be taken only by way of example, and not to otherwise limit the scope of this invention.
Exemplary embodiments have been disclosed above and illustrated in the accompanying drawings. It will be understood by those skilled in the art that various changes, omissions and additions may be made to that which is specifically disclosed herein without departing from the spirit and scope of the present invention.
